Power tells us the probability of rejecting the null hypothesis for an effe
ct of a given size and helps us select an appropriate design prior to runni
ng the experiment. The key to computing power for an effect is determining
the size of the effect. We describe a general approach for sizing effects t
hat covers a wide variety of designs including factorials with categorical
levels, response surfaces, mixtures and crossed designs. Copyright (C) 2001
John Wiley & Sons, Ltd.